The document summarizes a study that estimates the total economic burden of prescription opioid overdose, abuse, and dependence in the United States in 2013 was $78.5 billion. Over one third of this cost ($28.9 billion) was due to increased healthcare and substance abuse treatment costs. Approximately one quarter of the total cost was borne by the public sector through healthcare, substance abuse treatment, and criminal justice costs. The study utilized national data on opioid overdose deaths and abuse/dependence prevalence to estimate costs across multiple sectors including healthcare, substance abuse treatment, criminal justice, and lost productivity.

1. The Economic Burden of Prescription Opioid Overdose,
Abuse, and Dependence in the United States, 2013
Curtis S. Florence, PhD, Chao Zhou, PhD, Feijun Luo, PhD, and Likang Xu, MD
Importance: It is important to understand the magnitude and dis-
tribution of the economic burden of prescription opioid overdose,
abuse, and dependence to inform clinical practice, research, and
other decision makers. Decision makers choosing approaches to
address this epidemic need cost information to evaluate the cost
effectiveness of their choices.
Objective: To estimate the economic burden of prescription opioid
overdose, abuse, and dependence from a societal perspective.
Design, Setting, and Participants: Incidence of fatal prescription
opioid overdose from the National Vital Statistics System, prevalence
of abuse and dependence from the National Survey of Drug Use and
Health. Fatal data are for the US population, nonfatal data are a na-
tionally representative sample of the US civilian noninstitutionalized
population ages 12 and older. Cost data are from various sources
including health care claims data from the Truven Health MarketScan
Research Databases, and cost of fatal cases from the WISQARS (Web-
based Injury Statistics Query and Reporting System) cost module.
Criminal justice costs were derived from the Justice Expenditure and
Employment Extracts published by the Department of Justice. Esti-
mates of lost productivity were based on a previously published study.
Exposure: Calendar year 2013.
Main Outcomes and Measures: Monetized burden of fatal over-
dose and abuse and dependence of prescription opioids.
Results: The total economic burden is estimated to be $78.5 billion.
Over one third of this amount is due to increased health care and
substance abuse treatment costs ($28.9 billion). Approximately one
quarter of the cost is borne by the public sector in health care,
substance abuse treatment, and criminal justice costs.
Conclusions and Relevance: These estimates can assist decision
makers in understanding the magnitude of adverse health outcomes
associated with prescription opioid use such as overdose, abuse, and
dependence.
Key Words: prescription opioid, overdose, abuse and dependence,
economic burden
(Med Care 2016;54: 901–906)
The adverse health effects of the misuse of prescription
opioids, including abuse, dependence, and overdose are a
well-documented public health problem.1 Fatal prescription
drug overdoses have been described as an epidemic by the
US Centers for Disease Control and Prevention.2 Pre-
scription opioids account for approximately 70% of fatal
prescription drug overdoses.3,4
Decision makers at both the federal and state levels
have responded to the epidemic with several strategies aimed
at reducing the burden of the epidemic. For example, in 2011
the US Office of National Drug Control Policy issued a set of
recommendations that, in part, call for all states to have
functional prescription drug monitoring programs (PDMPs),
and encourages federal agencies such as the Veterans Ad-
ministration to share data with state PDMPs when legally
permitted to do so.5 Policies such as these face a difficult task
in addressing the overdose epidemic while balancing the care
of patients who need treatment for pain. Also, decision
makers in government and the health care sector face fi-
nancial constraints that require strategies that are cost effi-
cient as well as effective, while also considering the resource
use for addressing other social and health problems.
An essential component in identifying prevention
strategies that are cost-effective is understanding the eco-
nomic burden produced by the adverse health outcomes.
Previous researchers have estimated the overall societal
impact of prescription opioid misuse.6,7 Other studies have
examined specific components of the overall issue of opioid
misuse, such as the cost of poisonings, nonmedical use,8 and
abuse and workplace absenteeism.9 Most recently, Birnbaum
et al10 estimated the overall societal impact of prescription
opioid abuse, dependence, and misuse in the United States to
be $55.7 billion in 2007. Since that year, however, the epi-
demic has continued to progress. From 2007 to 2013, the
annual number of prescription opioid overdose deaths has
increased by over 1800 cases,3 and the annual number of
persons who abuse or are dependent on prescription opioids
has increased by over 200,000 persons.11
In this study, we present updated estimates of the
economic burden of prescription opioid overdose, abuse, and
From the National Center for Injury Prevention and Control, Centers for
Disease Control and Prevention, Atlanta, GA.
The findings and conclusions in this report are those of the authors and do
not necessarily represent the official position of the Centers for Disease
Control and Prevention.
The authors declare no conflict of interest.
Reprints: Curtis S. Florence, PhD, National Center for Injury Prevention and
Control, Centers for Disease Control and Prevention, 4770 Buford
Highway NE, Mailstop F-62, Atlanta, GA 30341. E-mail: cflorence@
cdc.gov.
Supplemental Digital Content is available for this article. Direct URL cita-
tions appear in the printed text and are provided in the HTML and PDF
versions of this article on the journal’s Website www.lww-medical
care.com.
Copyright r 2016 Wolters Kluwer Health, Inc. All rights reserved.
ISSN: 0025-7079/16/5410-0901
ORIGINAL ARTICLE
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2. dependence for 2013 using the most recently available data.
We also incorporate more comprehensive health care
spending data than previous studies, and we use recently
updated methods for valuing the loss of productivity (both
through employment and household activities) for fatal and
nonfatal cases.
METHODS
Overview
In this study, we calculated cost estimates of pre-
scription opioid overdose, abuse, and dependence based on
the incidence of overdose deaths and the prevalence of
prescription opioid abuse and dependence for calendar year
2013. We considered a societal perspective, which means we
considered both the cost for persons experiencing overdose
or abuse/dependence, and costs incurred by society in gen-
eral, such as criminal justice–related costs. The cost com-
ponents that considered were health care and substance abuse
treatment cost, criminal justice cost, and lost productivity.
Costs calculated for abuse and dependence are annual costs,
while costs for fatal cases are lifetime costs discounted to
2013 present value at a rate of 3%. We used the most re-
cently available year of data for all cost components. When
the most recent year of data available was earlier than 2013,
costs were inflation-adjusted to 2013 dollars.
Our measure of incidence of prescription opioid
overdose deaths in 2013 came from the CDC WONDER
database, which records all deaths reported in the United
States National Vital Statistics System.3 Cases were identi-
fied using the multiple cause of death ICD-10 codes (T40.2–
T40.4), which identify deaths across all intents (uninten-
tional, intentional, and undetermined). Prevalence of pre-
scription opioid abuse and dependence was measured using
the 2013 National Survey on Drug Use and Health
(NSDUH). The NSDUH is a nationally representative sam-
ple of the US civilian noninstitutionalized population ages 12
and older. The survey collects detailed information on sub-
stance use, including a questionnaire that can identify abuse
and dependence based on the Diagnostic and Statistical
Manual of Mental Disorders, 4th edition (DSM-IV)12 defi-
nition for a variety of substances, including prescription
opioids. The survey also collects detailed data on health in-
surance coverage during the year, and basic demographic
information such as sex and age. This information was used
in assigning health care costs and lost productivity costs to
abuse/dependence cases, as described in more detail below.
Survey weights were included in the data that allow for es-
timation of nationally representative population totals for
cases of substance abuse and dependence. Details of all
calculations presented below may be found in the electronic
appendix (Supplemental Digital Content 1, http://links.
lww.com/MLR/B261) that accompanies this study.
Health Care Costs
A matched case-control design was used to estimate
the impact of prescription opioid abuse diagnoses on health
care spending. This design was implemented using the de-
identified Truven Health MarketScan Research Databases for
commercial, Medicaid, and Medicare health plan enrollees.
The MarketScan data capture person-specific utilization,
expenditures, and enrollment across inpatient, outpatient,
and prescription drug claims. The commercial database in-
cludes private-sector health data from approximately 100
different insurance companies. The Medicare database
contains claims for Medicare-eligible retirees with employer-
sponsored Medicare Supplemental plans, and include
expenditures buy both Medicare and supplemental coverage.
The MarketScan Medicaid Database contains the pooled
health care experience of approximately 7 million Medicaid
enrollees from 11 geographically dispersed states. The
Medicaid data does not identify the states included to pre-
serve confidentiality. As the estimation strategy requires the
comparison of expenditures at an individual level, the anal-
ysis excludes those in capitated payment plans.
Prescription opioid abuse and dependence cases were
identified using previously described methodology.13 Diagnosed
commercial, Medicare, and Medicaid cases were identified as
patients with Z1 diagnosis for opioid abuse or dependence
during the third quarter of 2011 through the fourth quarter of
2012, defined using International Classification of Diseases,
Ninth Revision, Clinical Modification (ICD-9-CM) codes for
opioid abuse, dependence or overdose [304.0X (opioid type
dependence), 304.7X (combinations of opioid type dependence
with any other drug dependence), 305.5X (nondependent opioid
abuse), 965.00 (poisoning by opium (alkaloids) unspecified),
965.02 (poisoning by methadone), and 965.09 (poisoning by
other opiates and related narcotics)]. It is not possible to dis-
tinguish prescription opioid dependence from heroin depend-
ence with the ICD-9 codes. The implications of this limitation of
the data for our results will be discussed below.
All patients were continuously eligible with non-
capitated plan coverage during the 18-month study period.
The 18-month study period consisted of a 12-month ob-
servation period with first diagnosis as the index date, and a
6-month baseline period preceding the observation period
that was used for propensity score matching. For example, if
patient A’s first abuse diagnosis date was February 1, 2012
then this date was considered the index date for this patient.
Then the 18-month study period included the 6-month
baseline period (August 1, 2011–January 31, 2012) before
the index date and the 12-month period (February 1, 2012–
January 31, 2013) after the index date. For the comparison
patients, the index date was assigned as the date of a random
medical claim, and the data were then organized around this
date by the same method.
To account for baseline differences in demographics,
comorbidities, and health care resource use, abusers were
matched 1:1 to comparison patients based on propensity scores
estimated using a logistic regression model for all study pa-
tients. For commercial and Medicare analyses, the regression
model included age, sex (male/female), baseline health care
costs, Charlson comorbidity index, region of patient residence
(Northeast, North Central, South, West), and plan type (eg,
Exclusive Provider Organization, Health Maintenance Organ-
ization, Non-Capitated Point-of-Service, etc.) as independent
variables. Because Medicaid has slightly different variables, the
logistic regression used following variables: age, sex (male/
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3. female), race (white, black, Hispanic, and other), baseline
health care costs, Charlson comorbidity index, Medicare eligi-
bility, basis of eligibility (eg, low income child), and plan type
(eg, basic/major medical, comprehensive, Exclusive Provider
Organization, Health Maintenance Organization, Preferred
Provider Organization, etc.). The cost was total health care cost
including inpatient and outpatient care and all prescription
drugs. Health care costs in the 12-month observation period
were compared between abuse or dependence cases and
matched comparison patients to determine the excess annual
per patient health care costs. Nonlinear regression models were
estimated to account for the skewed nature of the health care
expenditure data (gamma family with log link), with ex-
penditures as the dependent variable and an indicator for di-
agnosed or control group as the independent variable.14
Excess medical and drug per-patient costs were then
multiplied by the relevant number of opioid abuse patients
derived from the NSDUH for each insurance coverage cat-
egory reported in the survey data (Private, Medicare, Med-
icaid, CHAMPUS/VA, other, and uninsured). CHAMPUS/
VA and other categories were assigned costs for private
coverage. The uninsured were assigned 50% of the cost of
private insurance, based on reports that show this to be the
typical ratio of spending for the uninsured population.15
While the estimation described above will account for
the cost of health services reimbursed by insurance plans for
those diagnosed with opioid use disorder, there are other
sources of payment for substance abuse treatment that are
important to measure. Substance abuse treatment costs that
were not paid by health insurance (such as public programs
like SAMSHA block grants and private foundation funding)
were calculated by identifying non–insurance-based federal,
state, local, and private expenditures on substance abuse
treatment.11 These costs were multiplied by the share of drug
abuse and dependence cases associated with prescription
opioids in the 2013 NSDUH.
Criminal Justice Costs
We followed an apportionment approach previously
described10 to update criminal justice costs to 2013.16 This
method consists of using reported criminal justice spending
for drug crimes and multiplying that number by the share of
drug abuse and dependence cases represented by prescription
opioids from NSDUH. The criminal justice costs consisted of
4 components: (1) police protection, (2) legal and ad-
judication, (3) correctional facilities, and (4) property lost
due to crimes. We obtained spending data on police pro-
tection, legal and adjudication activities, and correctional
facilities from the Justice Expenditure and Employment
Extracts, 2012—Preliminary17 and data on property lost due
to crimes from the Crime in the United States 2012.18 We
replicated the calculation procedures by Birnbaum and col-
leagues to estimate the proportions of these 3 components
attributable to prescription opioid abuse or dependence: the
ratio of arrests for the components of police protection and
legal and adjudication,18–21 the ratio of incarcerations for the
correctional facilities component,22 and the ratio for the
component of property lost due to crimes.
Lost Productivity Costs
We considered lost productivity costs from: (1) pre-
mature death from prescription opioid abuse or dependence,
(2) reduced productive hours for abuse/dependence, and (3)
incarceration. We estimated the cost of fatal opioid abuse or
dependence by entering the number of prescription opioid
overdose deaths in 2013 into the Cost of Injury Reports
application under CDC’s WISQARS (Web-based Injury
Statistics Query and Reporting System) cost module.23 The
WISQARS cost module estimates the lost productivity of a
fatal injury based on the sex and age of the decedent and the
mechanism of injury. Cost are assigned based on the earn-
ings expected for a person of the decedent’s sex and age over
the remaining expected lifespan. We used the cost estimate
for those that died from poisonings, for all intents.
In calculating lost productivity for abuse and depend-
ence, we used an approach that values the loss of “productive
hours.” Productive hours are any time that is spent in paid
employment or household productivity. The measure of
production value used estimated the average time spent in
employment and household production and estimated the
value (including fringe benefits) of this time by age and sex
category. This value then was multiplied by the percentage
reduction in productivity attributable to drug abuse/depend-
ence (17% for males and 18% for females24), and finally
summed over values across all sex and age groups. The
prevalence of prescription opioid abuse/dependence cases for
each sex and age group were tabulated from the 2013
NSDUH, then multiplied that by the corresponding per
person annual production value of US population,25 which
was inflated to 2013 dollars.
To calculate lost productivity due to incarceration, we
first used the numbers of inmates incarcerated for crimes
attributed to prescription opioid abuse/dependence at federal,
state, and local levels in 2013. After estimating the numbers
of federal, state, and local inmates incarcerated for crimes
attributed to prescription opioid abuse or dependence, we
then multiplied those numbers by the per person annual
production value of the US population inflated to 2013
dollars.
Finally, a sensitivity analysis for all major cost cate-
gories was conducted. This was done by calculating the cost
numbers at the endpoints of the 95% confidence interval of
both the prevalence of prescription opioid abuse and de-
pendence and the number of prescription opioid deaths.
RESULTS
Table 1 reports the prevalence of prescription opioid
abuse and dependence, and the number of fatal overdoses
from prescription opioids in 2013. Almost 2 million people
are estimated to meet the DSM-IV criteria for abuse and
dependence, and over 16,000 died from prescription opioid
overdoses. Both of these numbers represent a substantial
increase from the most recently published comprehensive
cost estimates from 2007, with the number of fatal cases over
1800 higher, and the prevalence of abuse and dependence
increased by approximately 200,000 persons.
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4. Table 2 reports the estimates of annual health care cost
differences for patients diagnosed with opioid abuse or de-
pendence and their matched comparisons. The cost differ-
ences for all 3 types of insurance are large and statistically
significant. Medicare has the largest cost difference at over
$17,000. Private insurance has a cost increase of $15,500,
and Medicaid is over $13,700. Full regressions results for the
propensity score matching and health care expenditure re-
gressions are available in the electronic appendix (Supple-
mental Digital Content 2, http://links.lww.com/MLR/B262).
The aggregate costs associated with fatal overdose and
abuse/dependence cases, and the range of estimates based on
the variation in estimated abuse, dependence, and overdose
outcomes, are reported in Table 3. The aggregate cost for
these prescription opioid–related overdose, abuse, and de-
pendence was over $78.5 ($70.1–$87.3) billion. Almost two
thirds of these costs were related to health care, substance
abuse treatment, and lost productivity for nonfatal cases
(Fig. 1). Total spending for health care and substance abuse
treatment accounted for over $28 billion [$26.1 ($21.4–30.8)
billion from insurance and $2.8 ($2.6–$3.2) billion from
other sources]. Fatal cases account for a little more than one
quarter of the costs [$21.5 ($21.2–$21.8) billion].
The aggregate costs demonstrate the substantial
amount of the economic burden that is borne by federal,
state, and local government. Over 14% of the cost is funded
by public health insurance programs (Medicare, Medicaid,
and Champus/VA), and 3.2% is from additional government
sources for substance abuse treatment. Almost all of the
criminal justice–related costs (96%, or $7.3 of $7.7 billion)
goes to activities directly funded by state and local govern-
ment. Taken together, this means that almost 25% of the
aggregate economic burden is funded by public sources. In
addition, some portion of the lost earnings will be borne by
the public sector in the form of forgone tax revenue.
DISCUSSION
The analysis presented here is subject to several limi-
tations. In some cases, these limitations can help identify
areas for further research that will improve our under-
standing of the impact of the prescription opioid overdose
epidemic. For example, our estimates of nonfatal costs are
based on the prevalence of abuse and dependence. Ideally,
the economic burden of an adverse health outcome would be
estimated by calculating the lifetime cost of the condition—
that is, observing the condition from its onset until it ends.
Then, the total value of preventing the condition from oc-
curring would be known. At the present time though, in-
formation in the research literature about the natural history
of opioid misuse does not allow for such a calculation, and
surveillance systems are not in place to adequately measure
the incidence of the condition in the population.
TABLE 1. Prevalence of Prescription Opioid Abuse and
Dependence, and Fatal Overdose, United States 2013
Outcome
Cases in 2013* (95%
Confidence Interval)
Prescription opioid abuse and
dependence (Millions)
1.935 (1.586, 2.284)
Fatal overdose (no. deaths)w
16,235 (15,985, 16,485)
*National Survey of Drug Use and Health, 2013.
w
CDC WONDER database, ICD-10 Multiple Cause of Death Codes.
TABLE 2. Estimated Annual Health Insurance Cost Increase
After Diagnosis With Prescription Opioid Misuse Disorder—
MarketScan Commercial, Medicare, and Medicaid Databases,
United States (2013 Dollars)
Estimated Incremental Effect
(95% Confidence Interval)
Private health insurance (N = 116,225) $15,500 ($14,922, $16,078)
Medicare (N = 6917) $17,052 ($13,472, $20,632)
Medicaid (N = 30,454) $13,743 ($12,341, $15,145)
TABLE 3. Aggregate Societal Costs of Prescription Opioid
Abuse, Dependence, and Fatal Overdose, United States
(Millions of 2013 Dollars)
Nonfatal Costs
Aggregate Costs (Range
Based on 95% CI of
Prevalence)
Percentage of
Aggregate
Costs
Health care
Private insurance $14,041 17.9
Medicare $2593* 3.3
Medicaid $5490* 7.0
Champus/VA $428* 0.5
Other $1003 1.3
Uninsured $2519 3.2
Total $26,075 ($21,372–$30,778) 33.2
Substance abuse treatment
Federal $721* 0.9
State and local $1823* 2.3
Private $276 0.4
Total $2820 ($2567–$3245) 3.6
Criminal justice
Police protection $2812* 3.6
Legal and
adjudication
$1288* 1.6
Correctional
facilities
$3218* 4.1
Property lost due to
crime
$335 0.4
Total criminal justice
costs
$7654 (*) 9.7
Lost productivity
Reduced productive
time/increased
disability
$16,262 ($13,329–$19,195) 20.7
Production lost for
incarcerated
individuals
$4180 ($3957–$4556) 5.3
Total $20,441 ($17,286–$23,751) 26.0
Total nonfatal costs $56,990 ($48,879–$65,428) 72.6
Fatal costs
Lost productivity $21,429 27.3
Health care $84 0.1
Total fatal costs $21,513 ($21,182–$21,844) 27.4
Total of nonfatal and
fatal
$78,503 ($70,061–$87,272) 100.0
*Public sector costs.
CI indicates confidence interval.
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5. Our health care cost estimates used the definition of
opioid abuse and dependence identified by ICD-9 diagnosis
codes. This definition does not differentiate between pre-
scription opioids and heroin. Because we are interested in the
difference in health care spending between those with abuse
or dependence and those without, this will bias our results if
prescription opioid abuse or dependence and heroin abuse or
dependence have different effects on health care spending.
On the basis of responses to the NSDUH, prescription opioid
abuse and dependence is far more common than for heroin.
In 2013, an estimated 1.9 million people reported pre-
scription opioid abuse or dependence, whereas 517,000 re-
ported heroin abuse or dependence (with some reporting
both). If diagnosed opioid abuse/dependence follows a pat-
tern similar to NSDUH responses and heroin and pre-
scription opioid users are equally likely to be treated, the
effect on our estimates should be mitigated.
Finally, we did not attempt to attribute costs to specific
drugs if multiple types of drug abuse were reported. This
could bias our results if the health care cost impact of abuse
and dependence is different between prescription opioids and
heroin, or if abuse of prescription opioids alone has a dif-
ferent effect from abuse of multiple drugs. We are also un-
able to account for the impact of diversion of drugs for
nonmedical use. Future research could analyze whether this
is the case using data that allows for these different sources
of abuse and dependence to be identified.
We also estimated the per case health care cost impact
using a convenience sample of persons enrolled in com-
mercial insurance plans, Medicare plans with an employer-
sponsored supplemental plan, and subset of state Medicaid
plans. The populations covered by these plans are not rep-
resentative of the US population, and also may not be rep-
resentative of the populations most at risk for opioid
overdose, abuse, or dependence. For example, many people
receiving Social Security disability payments are covered by
both Medicare and Medicaid. In our analysis, health care
spending was only measured for 1 health insurance plan for
each dependent person. In the case of these “dual eligible”
patients, our health care cost estimates will be too low. We
also depended on medical diagnosis of abuse and depend-
ence, which could underreport the true rate.
Finally, it is extremely difficult to measure all costs to
society from an epidemic. In this case, there are many costs
we were unable to measure, such as the reduction in quality
of life of those who are dependent. These impacts are sub-
stantial, with a previous study finding a quality-adjusted life
year reduction of approximately 50%.26 We also cannot
account for the pain and suffering of family members who
have lost loved ones due to fatal overdoses. The costs that we
can identify, however, do help increase our understanding of
the impact of the epidemic.
The economic burden estimates presented here help to
quantify some of the adverse health impacts associated with
prescription opioids. In the ideal case, decision makers could
use these estimates when weighing the benefits and risks of
using opioids to treat pain, and evaluating prevention mea-
sures to reduce harmful use. However, at the present time a
full accounting of both the benefits and costs of prescription
opioid use is not available.
The results presented here are also helpful in understanding
the distribution of the economic burden. A large share of the cost
Fatal Cost (Lost productivity and
Health Care)
27%
Criminal Justice
10%
Lost Productivity (Nonfatal)
26%
Substance Abuse Treatment
4%
Private Insurance
18%
Medicare
3%
Medicaid
7%
CHAMPUS/VA Other
2%
Uninsured
3%
Health
Insurance
[PERCENTAGE]
DISTRIBUTION OF THE ECONOMIC BURDEN OF PRESCRIPTION OPOID OVERDOSE,
ABUSE AND DEPENDENCE
FIGURE 1. Distribution of the economic burden of prescription opioid overdose, abuse, and dependence.
Medical Care Volume 54, Number 10, October 2016 Economic Burden of Prescription Opioid Overdose
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6. is borne by the public sector, both through direct services from
government agencies, but also through tax revenue that will be
lost from reduced earnings. Also, the health care sector bears
approximately one third of the costs we have estimated here.
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Florence et al Medical Care Volume 54, Number 10, October 2016
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